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resnext.py
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resnext.py
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'''
New for ResNeXt:
1. Wider bottleneck
2. Add group for conv2
'''
import torch.nn as nn
import math
__all__ = ['ResNeXt', 'resnext18', 'resnext34', 'resnext50', 'resnext101',
'resnext152']
def conv3x3(in_planes, out_planes, stride=1):
"""3x3 convolution with padding"""
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=1, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, num_group=32):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes*2, stride)
self.bn1 = nn.BatchNorm2d(planes*2)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes*2, planes*2, groups=num_group)
self.bn2 = nn.BatchNorm2d(planes*2)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, num_group=32):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes*2, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes*2)
self.conv2 = nn.Conv2d(planes*2, planes*2, kernel_size=3, stride=stride,
padding=1, bias=False, groups=num_group)
self.bn2 = nn.BatchNorm2d(planes*2)
self.conv3 = nn.Conv2d(planes*2, planes * 4, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNeXt(nn.Module):
def __init__(self, block, layers, num_classes=1000, num_group=32):
self.inplanes = 64
super(ResNeXt, self).__init__()
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0], num_group)
self.layer2 = self._make_layer(block, 128, layers[1], num_group, stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], num_group, stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], num_group, stride=2)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, num_group, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, num_group=num_group))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, num_group=num_group))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def resnext18( **kwargs):
"""Constructs a ResNeXt-18 model.
"""
model = ResNeXt(BasicBlock, [2, 2, 2, 2], **kwargs)
return model
def resnext34(**kwargs):
"""Constructs a ResNeXt-34 model.
"""
model = ResNeXt(BasicBlock, [3, 4, 6, 3], **kwargs)
return model
def resnext50(**kwargs):
"""Constructs a ResNeXt-50 model.
"""
model = ResNeXt(Bottleneck, [3, 4, 6, 3], **kwargs)
return model
def resnext101(**kwargs):
"""Constructs a ResNeXt-101 model.
"""
model = ResNeXt(Bottleneck, [3, 4, 23, 3], **kwargs)
return model
def resnext152(**kwargs):
"""Constructs a ResNeXt-152 model.
"""
model = ResNeXt(Bottleneck, [3, 8, 36, 3], **kwargs)
return model